{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T06:30:51.509765Z",
     "start_time": "2019-06-13T06:30:51.492818Z"
    }
   },
   "outputs": [],
   "source": [
    "# ================基于XGBoost原生接口的分类=============\n",
    "from sklearn.datasets import load_iris\n",
    "import xgboost as xgb\n",
    "from xgboost import plot_importance\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score   # 准确率\n",
    "# 加载样本数据集\n",
    "iris = load_iris()\n",
    "X,y = iris.data,iris.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234565) # 数据集分割\n",
    "\n",
    "# 算法参数\n",
    "params = {\n",
    "    #'booster': 'gbtree',\n",
    "    #'objective': 'multi:softmax',\n",
    "    'num_class': 3,\n",
    "    'gamma': 0.1,\n",
    "    'max_depth': 6,\n",
    "    'lambda': 2,\n",
    "    #'subsample': 0.7,\n",
    "    #'colsample_bytree': 0.7,\n",
    "    #'min_child_weight': 3,\n",
    "    #'silent': 1,\n",
    "    'eta': 0.1,\n",
    "    'seed': 1000,\n",
    "    #'nthread': 4,\n",
    "}\n",
    "\n",
    "plst = params.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T06:30:51.543418Z",
     "start_time": "2019-06-13T06:30:51.511908Z"
    }
   },
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(X_train, y_train) # 生成数据集格式\n",
    "num_rounds = 50\n",
    "model = xgb.train(plst, dtrain, num_rounds) # xgboost模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T06:30:51.724603Z",
     "start_time": "2019-06-13T06:30:51.545200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuarcy: 96.67%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对测试集进行预测\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "y_pred = model.predict(dtest)\n",
    "\n",
    "# 计算准确率\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"accuarcy: %.2f%%\" % (accuracy*100.0))\n",
    "\n",
    "# 显示重要特征\n",
    "plot_importance(model)\n",
    "plt.show()"
   ]
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